Avantos is building the AI-native operating system for financial services — transforming fragmented data into a single, intelligent system that powers workflows, automation, and decision-making.
We’re a product-led, fast-moving team at the intersection of AI, fintech, and modern infrastructure.
We’re looking for a Staff Data & Platform Engineer to build the data and core platform and infrastructure layer that powers our AI systems.
This is not just about pipelines — it’s about creating the systems, tooling, and abstractions that enable engineers and AI workflows to move fast, safely, and reliably.
Design and implement core backend systems and integrations that power the product.
Design and build our data platform (orchestration, pipelines, developer workflows)
Create scalable systems for data ingestion, transformation, and access
Design data models and APIs that won’t collapse under growth.
Make sane decisions about schemas, indexes, migrations, and performance.
Own infrastructure for data + AI workloads (containers, orchestration, cloud services)
Enable self-serve data workflows for engineering and product teams
Improve observability, reliability, and performance across the data stack
Support AI/ML systems (embeddings, vector data, model pipelines)
Build tooling for document processing, metadata, and normalization
7+ years in data engineering, platform engineering, or backend infrastructure
A track record of shipping and owning production systems.
Strong systems thinking (not just pipelines — platform mindset)
Backend system design and API design
Data modeling and relational databases
Performance, reliability, and failure modes
Proficiency in Golang, SQL, Terraform, AWS
Comfort in a fast-moving startup environment
LLMs / RAG, vector search, fintech or regulated systems
Have startup or early-stage experience.
Build the foundation layer of an AI-native product
High ownership + direct impact
Work where data, platform, and AI converge
You’re someone who doesn’t just move data — you build the systems that make data usable at scale.
Build and operate reliable infrastructure data products—including pipelines, models, and services—to support usage and cost insights, planning, attribution, and budgets while shaping technical direction across teams.
Lead the technical direction and hands-on evolution of a large-scale data platform, solving complex Spark, Iceberg, Kafka, and StarRocks challenges while mentoring engineers and improving reliability.
Set technical direction for large-scale compliance data and control systems, shaping roadmaps and architecture while improving detection effectiveness, auditability, reliability, and operational performance.
Leads customer migration engagements by setting technical direction and architecture, building Snowflake data solutions, and guiding teams through modernization, optimization, and AI initiatives.
Lead the design and operation of Azure Databricks lakehouse infrastructure, building reliable data pipelines and quality controls while guiding engineering practices and collaborating across teams to deliver business value.
